7 citations · 14 across the 30 of their papers we have counts for
15 papers · 1 filter
MultAttnAttrib: Training-Free Multimodal Attribution in Long Document Question Answering
Dang Quang Thien Tran, Quang V. Dang, Vinamra Tyagi +7
As grounded QA systems are increasingly deployed in AI assistants, accurately attributing generated answers to evidence is critical for user trust and model safety. While unimodal…
Enhancing Multilingual Embeddings via Multi-Way Parallel Text Alignment
Barah Fazili, Koustava Goswami
Multilingual pretraining typically lacks explicit alignment signals, leading to suboptimal cross-lingual alignment in the representation space. In this work, we show that training…
Decomposition-Enhanced Training for Post-Hoc Attributions In Language Models
Sriram Balasubramanian, Samyadeep Basu, Koustava Goswami +6
Large language models (LLMs) are increasingly used for long-document question answering, where reliable attribution to sources is critical for trust. Existing post-hoc attribution…
Subjective Behaviors and Preferences in LLM: Language of Browsing
Sai Sundaresan, Harshita Chopra, Atanu R. Sinha +4
A Large Language Model (LLM) offers versatility across domains and tasks, purportedly benefiting users with a wide variety of behaviors and preferences. We question this perception…
Beyond Logit Lens: Contextual Embeddings for Robust Hallucination Detection & Grounding in VLMs
Anirudh Phukan, Divyansh, Harshit Kumar Morj +3
The rapid development of Large Multimodal Models (LMMs) has significantly advanced multimodal understanding by harnessing the language abilities of Large Language Models (LLMs) and…
Enhancing Post-Hoc Attributions in Long Document Comprehension via Coarse Grained Answer Decomposition
Pritika Ramu, Koustava Goswami, Apoorv Saxena +1
Accurately attributing answer text to its source document is crucial for developing a reliable question-answering system. However, attribution for long documents remains largely un…
GenSco: Can Question Decomposition based Passage Alignment improve Question Answering?
Barah Fazili, Koustava Goswami, Natwar Modani +1
Retrieval augmented generation (RAG) with large language models (LLMs) for Question Answering (QA) entails furnishing relevant context within the prompt to facilitate the LLM in an…
Post-Hoc Answer Attribution for Grounded and Trustworthy Long Document Comprehension: Task, Insights, and Challenges
Abhilasha Sancheti, Koustava Goswami, Balaji Vasan Srinivasan
Attributing answer text to its source document for information-seeking questions is crucial for building trustworthy, reliable, and accountable systems. We formulate a new task of…
Peering into the Mind of Language Models: An Approach for Attribution in Contextual Question Answering
Anirudh Phukan, Shwetha Somasundaram, Apoorv Saxena +2
With the enhancement in the field of generative artificial intelligence (AI), contextual question answering has become extremely relevant. Attributing model generations to the inpu…
An Audit on the Perspectives and Challenges of Hallucinations in NLP
Pranav Narayanan Venkit, Tatiana Chakravorti, Vipul Gupta +5
We audit how hallucination in large language models (LLMs) is characterized in peer-reviewed literature, using a critical examination of 103 publications across NLP research. Throu…
Social Media Ready Caption Generation for Brands
Himanshu Maheshwari, Koustava Goswami, Apoorv Saxena +1
Social media advertisements are key for brand marketing, aiming to attract consumers with captivating captions and pictures or logos. While previous research has focused on generat…
Drilling Down into the Discourse Structure with LLMs for Long Document Question Answering
Inderjeet Nair, Shwetha Somasundaram, Apoorv Saxena +1
We address the task of evidence retrieval for long document question answering, which involves locating relevant paragraphs within a document to answer a question. We aim to assess…
Weakly-supervised Deep Cognate Detection Framework for Low-Resourced Languages Using Morphological Knowledge of Closely-Related Languages
Koustava Goswami, Priya Rani, Theodorus Fransen +1
Exploiting cognates for transfer learning in under-resourced languages is an exciting opportunity for language understanding tasks, including unsupervised machine translation, name…
SwitchPrompt: Learning Domain-Specific Gated Soft Prompts for Classification in Low-Resource Domains
Koustava Goswami, Lukas Lange, Jun Araki +1
Prompting pre-trained language models leads to promising results across natural language processing tasks but is less effective when applied in low-resource domains, due to the dom…
ULD@NUIG at SemEval-2020 Task 9: Generative Morphemes with an Attention Model for Sentiment Analysis in Code-Mixed Text
Koustava Goswami, Priya Rani, Bharathi Raja Chakravarthi +2
Code mixing is a common phenomena in multilingual societies where people switch from one language to another for various reasons. Recent advances in public communication over diffe…